{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 3: Word2vec - Learning Word Embeddings\n",
    "\n",
    "** Warning **:This code can consume a significant amount of memory. If you have significantly large RAM memory (>4GB) you do not need to worry. Otherwise please reduce the `batch_size` or `embedding_size` parameter to allow the model to fit in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\thushan\\documents\\python_virtualenvs\\tensorflow_venv\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "%matplotlib inline\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import bz2\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "import nltk # standard preprocessing\n",
    "import operator # sorting items in dictionary by value\n",
    "#nltk.download() #tokenizers/punkt/PY3/english.pickle\n",
    "from math import ceil\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "This code downloads a [dataset](http://www.evanjones.ca/software/wikipedia2text.html) consisting of several Wikipedia articles totaling up to roughly 61 megabytes. Additionally the code makes sure the file has the correct size after downloading it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified wikipedia2text-extracted.txt.bz2\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.evanjones.ca/software/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  if not os.path.exists(filename):\n",
    "    print('Downloading file...')\n",
    "    filename, _ = urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified %s' % filename)\n",
    "  else:\n",
    "    print(statinfo.st_size)\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "  return filename\n",
    "\n",
    "filename = maybe_download('wikipedia2text-extracted.txt.bz2', 18377035)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data without Preprocessing \n",
    "Reads data as it is to a string and tokenize it using spaces and returns a list of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 11631723\n",
      "Example words (start):  ['Propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing']\n",
      "Example words (end):  ['useless', 'for', 'cultivation', '.', 'and', 'people', 'have', 'sex', 'there', '.']\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  \"\"\"Extract the first file enclosed in a zip file as a list of words\"\"\"\n",
    "\n",
    "  with bz2.BZ2File(filename) as f:\n",
    "    data = []\n",
    "    file_string = f.read().decode('utf-8')\n",
    "    file_string = nltk.word_tokenize(file_string)\n",
    "    data.extend(file_string)\n",
    "  return data\n",
    "  \n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))\n",
    "print('Example words (start): ',words[:10])\n",
    "print('Example words (end): ',words[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data with Preprocessing with NLTK\n",
    "Reads data as it is to a string, convert to lower-case and tokenize it using the nltk library. This code reads data in 1MB portions as processing the full text at once slows down the task and returns a list of words. You will have to download the necessary tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data...\n",
      "Data size 3360286\n",
      "Example words (start):  ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing']\n",
      "Example words (end):  ['favorable', 'long-term', 'outcomes', 'for', 'around', 'half', 'of', 'those', 'diagnosed', 'with']\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  \"\"\"\n",
    "  Extract the first file enclosed in a zip file as a list of words\n",
    "  and pre-processes it using the nltk python library\n",
    "  \"\"\"\n",
    "\n",
    "  with bz2.BZ2File(filename) as f:\n",
    "\n",
    "    data = []\n",
    "    file_size = os.stat(filename).st_size\n",
    "    chunk_size = 1024 * 1024 # reading 1 MB at a time as the dataset is moderately large\n",
    "    print('Reading data...')\n",
    "    for i in range(ceil(file_size//chunk_size)+1):\n",
    "        bytes_to_read = min(chunk_size,file_size-(i*chunk_size))\n",
    "        file_string = f.read(bytes_to_read).decode('utf-8')\n",
    "        file_string = file_string.lower()\n",
    "        # tokenizes a string to words residing in a list\n",
    "        file_string = nltk.word_tokenize(file_string)\n",
    "        data.extend(file_string)\n",
    "  return data\n",
    "\n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))\n",
    "print('Example words (start): ',words[:10])\n",
    "print('Example words (end): ',words[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Dictionaries\n",
    "Builds the following. To understand each of these elements, let us also assume the text \"I like to go to school\"\n",
    "\n",
    "* `dictionary`: maps a string word to an ID (e.g. {I:0, like:1, to:2, go:3, school:4})\n",
    "* `reverse_dictionary`: maps an ID to a string word (e.g. {0:I, 1:like, 2:to, 3:go, 4:school}\n",
    "* `count`: List of list of (word, frequency) elements (e.g. [(I,1),(like,1),(to,2),(go,1),(school,1)]\n",
    "* `data` : Contain the string of text we read, where string words are replaced with word IDs (e.g. [0, 1, 2, 3, 2, 4])\n",
    "\n",
    "It also introduces an additional special token `UNK` to denote rare words to are too rare to make use of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 69215], ('the', 226881), (',', 184013), ('.', 120944), ('of', 116323)]\n",
      "Sample data [1731, 9, 8, 17161, 223, 4, 5224, 4496, 26, 11934]\n"
     ]
    }
   ],
   "source": [
    "# we restrict our vocabulary size to 50000\n",
    "vocabulary_size = 50000 \n",
    "\n",
    "def build_dataset(words):\n",
    "  count = [['UNK', -1]]\n",
    "  # Gets only the vocabulary_size most common words as the vocabulary\n",
    "  # All the other words will be replaced with UNK token\n",
    "  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "  dictionary = dict()\n",
    "\n",
    "  # Create an ID for each word by giving the current length of the dictionary\n",
    "  # And adding that item to the dictionary\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "    \n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  # Traverse through all the text we have and produce a list\n",
    "  # where each element corresponds to the ID of the word found at that index\n",
    "  for word in words:\n",
    "    # If word is in the dictionary use the word ID,\n",
    "    # else use the ID of the special token \"UNK\"\n",
    "    if word in dictionary:\n",
    "      index = dictionary[word]\n",
    "    else:\n",
    "      index = 0  # dictionary['UNK']\n",
    "      unk_count = unk_count + 1\n",
    "    data.append(index)\n",
    "    \n",
    "  # update the count variable with the number of UNK occurences\n",
    "  count[0][1] = unk_count\n",
    "  \n",
    "  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "  # Make sure the dictionary is of size of the vocabulary\n",
    "  assert len(dictionary) == vocabulary_size\n",
    "    \n",
    "  return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Batches of Data for Skip-Gram\n",
    "Generates a batch or target words (`batch`) and a batch of corresponding context words (`labels`). It reads `2*window_size+1` words at a time (called a `span`) and create `2*window_size` datapoints in a single span. The function continue in this manner until `batch_size` datapoints are created. Everytime we reach the end of the word sequence, we start from beginning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed']\n",
      "\n",
      "with window_size = 1:\n",
      "    batch: ['is', 'is', 'a', 'a', 'concerted', 'concerted', 'set', 'set']\n",
      "    labels: ['propaganda', 'a', 'is', 'concerted', 'a', 'set', 'concerted', 'of']\n",
      "\n",
      "with window_size = 2:\n",
      "    batch: ['a', 'a', 'a', 'a', 'concerted', 'concerted', 'concerted', 'concerted']\n",
      "    labels: ['propaganda', 'is', 'concerted', 'set', 'is', 'a', 'set', 'of']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch_skip_gram(batch_size, window_size):\n",
    "  # data_index is updated by 1 everytime we read a data point\n",
    "  global data_index \n",
    "    \n",
    "  # two numpy arras to hold target words (batch)\n",
    "  # and context words (labels)\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    \n",
    "  # span defines the total window size, where\n",
    "  # data we consider at an instance looks as follows. \n",
    "  # [ skip_window target skip_window ]\n",
    "  span = 2 * window_size + 1 \n",
    "    \n",
    "  # The buffer holds the data contained within the span\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  \n",
    "  # Fill the buffer and update the data_index\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  \n",
    "  # This is the number of context words we sample for a single target word\n",
    "  num_samples = 2*window_size \n",
    "\n",
    "  # We break the batch reading into two for loops\n",
    "  # The inner for loop fills in the batch and labels with \n",
    "  # num_samples data points using data contained withing the span\n",
    "  # The outper for loop repeat this for batch_size//num_samples times\n",
    "  # to produce a full batch\n",
    "  for i in range(batch_size // num_samples):\n",
    "    k=0\n",
    "    # avoid the target word itself as a prediction\n",
    "    # fill in batch and label numpy arrays\n",
    "    for j in list(range(window_size))+list(range(window_size+1,2*window_size+1)):\n",
    "      batch[i * num_samples + k] = buffer[window_size]\n",
    "      labels[i * num_samples + k, 0] = buffer[j]\n",
    "      k += 1 \n",
    "    \n",
    "    # Everytime we read num_samples data points,\n",
    "    # we have created the maximum number of datapoints possible\n",
    "    # withing a single span, so we need to move the span by 1\n",
    "    # to create a fresh new span\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
    "\n",
    "for window_size in [1, 2]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch_skip_gram(batch_size=8, window_size=window_size)\n",
    "    print('\\nwith window_size = %d:' %window_size)\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skip-Gram Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Hyperparameters\n",
    "\n",
    "Here we define several hyperparameters including `batch_size` (amount of samples in a single batch) `embedding_size` (size of embedding vectors) `window_size` (context window size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128 # Data points in a single batch\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "window_size = 4 # How many words to consider left and right.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors\n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "# We sample valid datapoints randomly from a large window without always being deterministic\n",
    "valid_window = 50\n",
    "\n",
    "# When selecting valid examples, we select some of the most frequent words as well as\n",
    "# some moderately rare words as well\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "valid_examples = np.append(valid_examples,random.sample(range(1000, 1000+valid_window), valid_size),axis=0)\n",
    "\n",
    "num_sampled = 32 # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Inputs and Outputs\n",
    "\n",
    "Here we define placeholders for feeding in training inputs and outputs (each of size `batch_size`) and a constant tensor to contain validation examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Training input data (target word IDs).\n",
    "train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "# Training input label data (context word IDs)\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "# Validation input data, we don't need a placeholder\n",
    "# as we have already defined the IDs of the words selected\n",
    "# as validation data\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Model Parameters and Other Variables\n",
    "We now define several TensorFlow variables such as an embedding layer (`embeddings`) and neural network parameters (`softmax_weights` and `softmax_biases`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables\n",
    "\n",
    "# Embedding layer, contains the word embeddings\n",
    "embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "\n",
    "# Softmax Weights and Biases\n",
    "softmax_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                        stddev=0.5 / math.sqrt(embedding_size))\n",
    ")\n",
    "softmax_biases = tf.Variable(tf.random_uniform([vocabulary_size],0.0,0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Model Computations\n",
    "\n",
    "We first defing a lookup function to fetch the corresponding embedding vectors for a set of given inputs. With that, we define negative sampling loss function `tf.nn.sampled_softmax_loss` which takes in the embedding vectors and previously defined neural network parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\thushan\\documents\\python_virtualenvs\\tensorflow_venv\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:1344: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Model.\n",
    "# Look up embeddings for a batch of inputs.\n",
    "embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
    "\n",
    "# Compute the softmax loss, using a sample of the negative labels each time.\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(\n",
    "        weights=softmax_weights, biases=softmax_biases, inputs=embed,\n",
    "        labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Word Similarities \n",
    "We calculate the similarity between two given words in terms of the cosine distance. To do this efficiently we use matrix operations to do so, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute the similarity between minibatch examples and all embeddings.\n",
    "# We use the cosine distance:\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(\n",
    "normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameter Optimizer\n",
    "\n",
    "We then define a constant learning rate and an optimizer which uses the Adagrad method. Feel free to experiment with other optimizers listed [here](https://www.tensorflow.org/api_guides/python/train)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Optimizer.\n",
    "optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Skip-Gram Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2000: 3.991611\n",
      "Average loss at step 4000: 3.627553\n",
      "Average loss at step 6000: 3.583732\n",
      "Average loss at step 8000: 3.513987\n",
      "Average loss at step 10000: 3.492103\n",
      "Nearest to his: indifferentism, amnesty, ethnographic, sheesh, banking, scot, bran, chamillionaire,\n",
      "Nearest to a: the, manchukuo, an, archipelagos, deaf, fins, communion, —,\n",
      "Nearest to it: this, outlying, not, 296, goats, messenger, reconstruct, socialized,\n",
      "Nearest to from: 1933., of, rap, dietitians, and, blanc, agraristas, technicians,\n",
      "Nearest to at: crevices, crates, alchemist, non-western, fasteners, hurdle, branch, melvins,\n",
      "Nearest to 's: the, had, offering, with, cheetham, update, horizontally, panzerfaust,\n",
      "Nearest to with: inerrancy, endeavour, belcher, prieta, and, f3, 's, helical,\n",
      "Nearest to ,: ., in, and, the, on, speeches, UNK, to,\n",
      "Nearest to this: it, boundary, luminescent, nitrate, furnace, tahiti, gem-quality, ethnographer,\n",
      "Nearest to not: lapu-lapu, sucked, it, victorians, warm, shortened, unapproved, asceticism,\n",
      "Nearest to to: for, ipu, speeds, and, dana, w.d, ., would,\n",
      "Nearest to has: have, wind, resisting, stomp, uíge, car, had, wingman,\n",
      "Nearest to .: ,, and, in, the, of, to, mussolini, sharply,\n",
      "Nearest to of: speeches, from, in, by, ., connected, gömbös, curd,\n",
      "Nearest to :: daqing, ayub, dialing, calorie, duna, centres, creeks, baal,\n",
      "Nearest to be: chance, distinguish, aretaic, 0.05, parting, 1343, wilson, 1488,\n",
      "Average loss at step 12000: 3.440032\n",
      "Average loss at step 14000: 3.433367\n",
      "Average loss at step 16000: 3.382702\n",
      "Average loss at step 18000: 3.407460\n",
      "Average loss at step 20000: 3.386678\n",
      "Nearest to his: half-light, microphone, fruiting, papadopoulos, lansdowne, indifferentism, attending, pliska,\n",
      "Nearest to a: the, an, outpacing, oedipus, lebak, letters, excommunicated, sovereigns,\n",
      "Nearest to it: this, he, vaise, sensitivity, cd-rom, there, sanz, layton,\n",
      "Nearest to from: in, to, agraristas, sanitation, between, lycée, of, manby,\n",
      "Nearest to at: fire, debacle, neutralized, santosuosso, epitheta, ufdd, inspect, branch,\n",
      "Nearest to 's: '', 800,000, athens, ishim, concretely, politicized, silo, its,\n",
      "Nearest to with: parche, trinidadians, cycles.most, or, diaspora, ligands, insurance, their,\n",
      "Nearest to ,: ., and, in, the, UNK, sheriffs, ), of,\n",
      "Nearest to this: it, boundary, markets, attains, an, dozens, pit-, 's,\n",
      "Nearest to not: medicean, lapu-lapu, also, advised, slightest, some, have, that,\n",
      "Nearest to to: would, from, in, for, ipu, saurischia, bernadino, designated,\n",
      "Nearest to has: is, have, had, bermuda, rushed, inaugurating, bishops, wellnhoferia,\n",
      "Nearest to .: ,, and, in, the, of, suspects, liège, donnchad,\n",
      "Nearest to of: the, ., and, in, by, ,, serf, kib,\n",
      "Nearest to :: dialing, brandy, railcar, ayub, monkey, kahneman, creeks, or,\n",
      "Nearest to be: have, that, were, take, thrill, conditional, jason, exclusively,\n",
      "Average loss at step 22000: 3.338528\n",
      "Average loss at step 24000: 3.394903\n",
      "Average loss at step 26000: 3.353220\n",
      "Average loss at step 28000: 3.376625\n",
      "Average loss at step 30000: 3.380557\n",
      "Nearest to his: their, 's, he, the, bug, gagnon, its, distributions,\n",
      "Nearest to a: an, olga, its, the, it, which, tobias, residents,\n",
      "Nearest to it: argument, not, this, sub-regions, he, a, so, nitrate,\n",
      "Nearest to from: between, into, prussian, supergroups, above-mentioned, hayes, overarching, due,\n",
      "Nearest to at: fire, tonal, incitement, explore, lyndon, in, debacle, monumental,\n",
      "Nearest to 's: his, the, its, phl, of, sinkhole, mid-century, mcdonald,\n",
      "Nearest to with: size, by, and, donck, store, emotionally, attendance, nuova,\n",
      "Nearest to ,: ., in, deportation, the, of, coached, exhibition, shepard,\n",
      "Nearest to this: boundary, 'quarter, it, superseded, masses, karachi, koch, preponderance,\n",
      "Nearest to not: n't, that, fritz, it, they, so, ascertaining, catering,\n",
      "Nearest to to: and, in, sandbar, messages, the, ,, would, forces,\n",
      "Nearest to has: had, have, is, was, trei, fragmentary, snapped, obedience,\n",
      "Nearest to .: ,, in, the, of, korda, and, 352, deleterious,\n",
      "Nearest to of: in, ,, sullivan-messmer, ., 's, resulted, prisons, and,\n",
      "Nearest to :: ;, 362, (, cucumbers, dharma, daqing, subtitle, railcar,\n",
      "Nearest to be: have, apus, persevered, it, thrill, crusaders, they, 316,\n",
      "Average loss at step 32000: 3.368088\n",
      "Average loss at step 34000: 3.341381\n",
      "Average loss at step 36000: 3.334178\n",
      "Average loss at step 38000: 3.342802\n",
      "Average loss at step 40000: 3.335163\n",
      "Nearest to his: 's, he, bug, half-light, their, her, richfield, 8500,\n",
      "Nearest to a: the, an, ,, hairs, greece, duels, and, micro-climates,\n",
      "Nearest to it: this, he, be, not, so, diminished, that, temperaments,\n",
      "Nearest to from: into, prussian, 1874, between, bicycling, authorized, and, qing,\n",
      "Nearest to at: anxiety, broadcast, schabowski, manitou, irregulars, inter-american, valhalla, counterrevolutionaries,\n",
      "Nearest to 's: his, #, her, blindly, ii, the, john, was,\n",
      "Nearest to with: gilded, chakvetadze, coca-cola, trinidadians, barbarity, 443, turf, indecipherable,\n",
      "Nearest to ,: ., and, of, UNK, in, the, a, exhibition,\n",
      "Nearest to this: it, furs, another, bobcats, exchanging, monash, 'quarter, wage,\n",
      "Nearest to not: they, that, n't, it, alps, siadh, could, catering,\n",
      "Nearest to to: facades, would, explore, competence, nesting, dimorphic, and, gustave,\n",
      "Nearest to has: had, have, is, fragmentary, pido, number-one, prohibiting, bermuda,\n",
      "Nearest to .: ,, and, of, the, alluding, helix, luftwaffe, competition,\n",
      "Nearest to of: ,, and, ., the, in, allahabad, bridesmaids, salting,\n",
      "Nearest to :: ;, minkel, newest, chinese, antennae, modelling, cavendish, exercises,\n",
      "Nearest to be: have, it, jenson, thrill, hirohito, decent, nationale, orchards,\n",
      "Average loss at step 42000: 3.330281\n",
      "Average loss at step 44000: 3.336652\n",
      "Average loss at step 46000: 3.341999\n",
      "Average loss at step 48000: 3.314822\n",
      "Average loss at step 50000: 3.311965\n",
      "Nearest to his: he, their, half-light, 's, plc, demoiselles, wrath, periplus,\n",
      "Nearest to a: the, an, this, note, long, eleusinian, prelate, cutting-edge,\n",
      "Nearest to it: he, also, not, this, atonal, supervision, pair, there,\n",
      "Nearest to from: and, into, in, between, härryda, divested, morwood, at,\n",
      "Nearest to at: incitement, neanderthals, in, maxtla, garrison, promiscuous, 8.4, from,\n",
      "Nearest to 's: his, the, enver, troy, trademarks, lie, depressed, ivlivs,\n",
      "Nearest to with: and, into, boers, rhapsody, breeding, then, handle, bluest,\n",
      "Nearest to ,: ., funnier, and, targeted, monte, 1900., in, pro-choice,\n",
      "Nearest to this: a, the, if, zp-32, criollismo, tobias, it, cultivates,\n",
      "Nearest to not: they, so, n't, could, spaceflight, siadh, monkeys, alps,\n",
      "Nearest to to: would, could, ealing, advice, can, that, did, assignments,\n",
      "Nearest to has: had, have, pido, fragmentary, is, baggara, uíge, also,\n",
      "Nearest to .: ,, that, and, but, in, whipple, by, revivalism,\n",
      "Nearest to of: in, bebop, salmonella, dàyuān, runners, ft., substitution, ,,\n",
      "Nearest to :: ;, '', (, [, UNK, acelhuate, philippine, or,\n",
      "Nearest to be: have, are, that, tee-shirts, hires, according, generations, nbc,\n",
      "Average loss at step 52000: 3.297227\n",
      "Average loss at step 54000: 3.303563\n",
      "Average loss at step 56000: 3.313289\n",
      "Average loss at step 58000: 3.298382\n",
      "Average loss at step 60000: 3.303299\n",
      "Nearest to his: her, 's, half-light, their, he, him, demoiselles, disposable,\n",
      "Nearest to a: an, the, this, duels, which, often, it, profitable,\n",
      "Nearest to it: this, only, be, what, he, there, drove, they,\n",
      "Nearest to from: into, in, sounded, on, ,, tyrell, carbonaceous, where,\n",
      "Nearest to at: debacle, after, shatt, morwood, schabowski, broadcast, accented, wrecking,\n",
      "Nearest to 's: his, its, s, hillsborough, r., 2.9, conservatively, fjords,\n",
      "Nearest to with: have, by, hearing, popularize, for, jurisdiction, assignments, and,\n",
      "Nearest to ,: ., the, in, and, warmongering, quiescence, UNK, noises,\n",
      "Nearest to this: it, a, the, another, 1889, longitudes, an, that,\n",
      "Nearest to not: so, n't, know, that, they, it, treatments, recognize,\n",
      "Nearest to to: would, soviet/russian, speeds, admittedly, p.m., could, prank, siemens,\n",
      "Nearest to has: had, have, is, pido, was, dcen, l, attempted,\n",
      "Nearest to .: ,, ;, :, the, chiyoda, distract, morphologies, of,\n",
      "Nearest to of: in, ., ,, dwellers, qipao, and, transiently, pencil,\n",
      "Nearest to :: ;, ., see, '', tough, antennae, franca, udine,\n",
      "Nearest to be: have, it, that, is, nbc, reins, lieberman, graeco-roman,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 62000: 3.302965\n",
      "Average loss at step 64000: 3.309494\n",
      "Average loss at step 66000: 3.269769\n",
      "Average loss at step 68000: 3.268404\n",
      "Average loss at step 70000: 3.284753\n",
      "Nearest to his: her, their, the, 's, its, an, retrieve, them,\n",
      "Nearest to a: an, the, this, that, bustle, angles, it, his,\n",
      "Nearest to it: this, that, he, what, not, if, be, only,\n",
      "Nearest to from: flaps, into, noah, in, x-2, sanitation, above-mentioned, psychosis,\n",
      "Nearest to at: impeachment, alma-ata, eliciting, palate, inter-american, promiscuous, hanaya, on,\n",
      "Nearest to 's: his, the, of, her, ', its, this, UNK,\n",
      "Nearest to with: conceptualised, and, very, abstractionist, by, limbs, like, bushing,\n",
      "Nearest to ,: and, ., UNK, in, the, to, as, of,\n",
      "Nearest to this: it, the, a, that, also, when, 's, its,\n",
      "Nearest to not: that, if, so, they, it, n't, because, be,\n",
      "Nearest to to: ,, could, for, and, help, in, ., vannevar,\n",
      "Nearest to has: have, had, was, is, pia, devín, humiliating, are,\n",
      "Nearest to .: ,, and, of, the, ;, that, as, by,\n",
      "Nearest to of: and, in, ., 's, for, ,, the, antiplatelet,\n",
      "Nearest to :: i, ;, ``, ,, that, '', tough, and,\n",
      "Nearest to be: have, crusaders, that, it, then, not, are, counterattack,\n",
      "Average loss at step 72000: 3.300587\n",
      "Average loss at step 74000: 3.298462\n",
      "Average loss at step 76000: 3.290271\n",
      "Average loss at step 78000: 3.309375\n",
      "Average loss at step 80000: 3.286403\n",
      "Nearest to his: her, 's, the, he, a, him, their, gathering,\n",
      "Nearest to a: an, the, his, ,, UNK, obstruct, and, another,\n",
      "Nearest to it: this, what, he, not, only, also, said, that,\n",
      "Nearest to from: between, into, lawful, flaps, through, in, cornices, superpower,\n",
      "Nearest to at: debacle, scoreboard, inter-american, detroit-windsor, participate, manitou, guardians, furtado,\n",
      "Nearest to 's: his, the, of, its, ', and, carole, 2002,\n",
      "Nearest to with: splendour, and, nexus-6, 260,000, aviv, had, ,, rong,\n",
      "Nearest to ,: ., and, UNK, (, ', the, of, a,\n",
      "Nearest to this: it, also, wage, the, throughput, tetrahedral, boom, swinging,\n",
      "Nearest to not: only, so, it, if, also, they, overlying, believe,\n",
      "Nearest to to: fusiliers, lejeune, for, would, élite, that, and, chilly,\n",
      "Nearest to has: had, have, is, was, 1644, since, essence, despite,\n",
      "Nearest to .: ,, ;, and, administering, three-month, coalfield, as, of,\n",
      "Nearest to of: ,, 's, bizarre, struggled, ., atheists, akmolinsk, among,\n",
      "Nearest to :: ;, crawdaddy, one, three, tough, two, wakka, all,\n",
      "Nearest to be: have, it, are, then, also, tee-shirts, hunua, infused,\n",
      "Average loss at step 82000: 3.297808\n",
      "Average loss at step 84000: 3.290011\n",
      "Average loss at step 86000: 3.295641\n",
      "Average loss at step 88000: 3.279056\n",
      "Average loss at step 90000: 3.270169\n",
      "Nearest to his: the, her, 's, he, allowable, a, their, she,\n",
      "Nearest to a: an, the, his, authorization, UNK, treatable, 've, weigh-in,\n",
      "Nearest to it: this, he, said, she, not, also, that, predominance,\n",
      "Nearest to from: between, positives, lawful, with, within, through, calvin, heta,\n",
      "Nearest to at: ccm, enjoys, counterrevolutionaries, franche-comté, tiers, taxing, 1904., malayo-polynesian,\n",
      "Nearest to 's: the, his, its, acrimonious, of, drifters, .for, crater,\n",
      "Nearest to with: and, for, her, 1530, ,, between, from, made,\n",
      "Nearest to ,: ., in, UNK, and, the, ;, (, of,\n",
      "Nearest to this: it, also, its, branched, another, greece, deficits, hst,\n",
      "Nearest to not: so, although, it, if, but, also, they, even,\n",
      "Nearest to to: sania, prank, would, with, will, place-names, for, subpixels,\n",
      "Nearest to has: had, have, since, is, devín, marcel, was, dcen,\n",
      "Nearest to .: ,, ;, that, and, the, dhexe, of, vain,\n",
      "Nearest to of: for, in, ,, ., and, with, 's, victors,\n",
      "Nearest to :: ;, newest, ``, three, include, wiesenthal, rexroth, entanglement,\n",
      "Nearest to be: have, billed, welles, pleas, crusaders, preordered, persevered, are,\n",
      "Average loss at step 92000: 3.298772\n",
      "Average loss at step 94000: 3.252265\n",
      "Average loss at step 96000: 3.286938\n",
      "Average loss at step 98000: 3.288883\n",
      "Average loss at step 100000: 3.275468\n",
      "Nearest to his: their, 's, work, he, david, her, its, him,\n",
      "Nearest to a: the, an, 's, this, first, spiders, another, pontus,\n",
      "Nearest to it: she, not, he, that, this, said, also, what,\n",
      "Nearest to from: between, in, calvin, during, about, over, up, 1587,\n",
      "Nearest to at: on, counterrevolutionaries, year, after, debacle, malayo-polynesian, broadcast, for,\n",
      "Nearest to 's: the, first, and, a, of, his, was, another,\n",
      "Nearest to with: between, after, while, and, like, from, rascia, qualities,\n",
      "Nearest to ,: ., and, in, of, the, on, ', to,\n",
      "Nearest to this: also, another, it, an, the, a, sinicized, vested,\n",
      "Nearest to not: it, still, so, n't, also, they, that, although,\n",
      "Nearest to to: would, ,, and, matched, of, will, could, uninteresting,\n",
      "Nearest to has: have, had, was, is, since, fifty, arterial, albrecht,\n",
      "Nearest to .: ,, ;, and, ', jefferson, that, ``, of,\n",
      "Nearest to of: ,, in, and, 's, on, album, the, .,\n",
      "Nearest to :: ;, three, two, nematode, one, (, '', ],\n",
      "Nearest to be: have, him, hunua, categorise, hear, billed, sedimentary, tee-shirts,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "skip_losses = []\n",
    "# ConfigProto is a way of providing various configuration settings \n",
    "# required to execute the graph\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as session:\n",
    "  # Initialize the variables in the graph\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  average_loss = 0\n",
    "  \n",
    "  # Train the Word2vec model for num_step iterations\n",
    "  for step in range(num_steps):\n",
    "    \n",
    "    # Generate a single batch of data\n",
    "    batch_data, batch_labels = generate_batch_skip_gram(\n",
    "      batch_size, window_size)\n",
    "    \n",
    "    # Populate the feed_dict and run the optimizer (minimize loss)\n",
    "    # and compute the loss\n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    \n",
    "    # Update the average loss variable\n",
    "    average_loss += l\n",
    "    \n",
    "    if (step+1) % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss = average_loss / 2000\n",
    "      \n",
    "      skip_losses.append(average_loss)\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step %d: %f' % (step+1, average_loss))\n",
    "      average_loss = 0\n",
    "    \n",
    "    # Evaluating validation set word similarities\n",
    "    if (step+1) % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      # Here we compute the top_k closest words for a given validation word\n",
    "      # in terms of the cosine distance\n",
    "      # We do this for all the words in the validation set\n",
    "      # Note: This is an expensive step\n",
    "      for i in range(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8 # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "        log = 'Nearest to %s:' % valid_word\n",
    "        for k in range(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log = '%s %s,' % (log, close_word)\n",
    "        print(log)\n",
    "  skip_gram_final_embeddings = normalized_embeddings.eval()\n",
    "\n",
    "# We will save the word vectors learned and the loss over time\n",
    "# as this information is required later for comparisons\n",
    "np.save('skip_embeddings',skip_gram_final_embeddings)\n",
    "\n",
    "with open('skip_losses.csv', 'wt') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    writer.writerow(skip_losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visulizing the Learnings of the Skip-Gram Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Only the Words Clustered Together Instead of Sparsely Distributed Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_clustered_embeddings(embeddings,distance_threshold,sample_threshold):\n",
    "    ''' \n",
    "    Find only the closely clustered embeddings. \n",
    "    This gets rid of more sparsly distributed word embeddings and make the visualization clearer\n",
    "    This is useful for t-SNE visualization\n",
    "    \n",
    "    distance_threshold: maximum distance between two points to qualify as neighbors\n",
    "    sample_threshold: number of neighbors required to be considered a cluster\n",
    "    '''\n",
    "    \n",
    "    # calculate cosine similarity\n",
    "    cosine_sim = np.dot(embeddings,np.transpose(embeddings))\n",
    "    norm = np.dot(np.sum(embeddings**2,axis=1).reshape(-1,1),np.sum(np.transpose(embeddings)**2,axis=0).reshape(1,-1))\n",
    "    assert cosine_sim.shape == norm.shape\n",
    "    cosine_sim /= norm\n",
    "    \n",
    "    # make all the diagonal entries zero otherwise this will be picked as highest\n",
    "    np.fill_diagonal(cosine_sim, -1.0)\n",
    "    \n",
    "    argmax_cos_sim = np.argmax(cosine_sim, axis=1)\n",
    "    mod_cos_sim = cosine_sim\n",
    "    # find the maximums in a loop to count if there are more than n items above threshold\n",
    "    for _ in range(sample_threshold-1):\n",
    "        argmax_cos_sim = np.argmax(cosine_sim, axis=1)\n",
    "        mod_cos_sim[np.arange(mod_cos_sim.shape[0]),argmax_cos_sim] = -1\n",
    "    \n",
    "    max_cosine_sim = np.max(mod_cos_sim,axis=1)\n",
    "\n",
    "    return np.where(max_cosine_sim>distance_threshold)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing the t-SNE Visualization of Word Embeddings Using Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting embeddings to T-SNE. This can take some time ...\n",
      "Pruning the T-SNE embeddings\n",
      "Out of  1000  samples,  352  samples were selected by pruning\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_points = 1000 # we will use a large sample space to build the T-SNE manifold and then prune it using cosine similarity\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "\n",
    "print('Fitting embeddings to T-SNE. This can take some time ...')\n",
    "# get the T-SNE manifold\n",
    "selected_embeddings = skip_gram_final_embeddings[:num_points, :]\n",
    "two_d_embeddings = tsne.fit_transform(selected_embeddings)\n",
    "\n",
    "print('Pruning the T-SNE embeddings')\n",
    "# prune the embeddings by getting ones only more than n-many sample above the similarity threshold\n",
    "# this unclutters the visualization\n",
    "selected_ids = find_clustered_embeddings(selected_embeddings,.25,10)\n",
    "two_d_embeddings = two_d_embeddings[selected_ids,:]\n",
    "\n",
    "print('Out of ',num_points,' samples, ', selected_ids.shape[0],' samples were selected by pruning')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the t-SNE Results with Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iJyIich25sNVA2a9LPxdRWaeB2iiicTn27ptWtn/t++/zeWAozJpZCJzCtWmU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MCBA4mNjSUyMlJJvzdipDoxBnJGjPwL2LdvH7t37+bQoUPExsbi4uKiFIyXhcFgUIyNO3XqpNT+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r164sWLCAtLQ0MjIy6NWrF0uXLjXvxYuLiwNMSzXXrVuHoteTaGdHQlwcSr9+KHp9Rd6auAsJ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h6qu+h3nWLqq58i6Vyg/7fkureKjNpJkiRJpchATpIkqZbytbAi1LE/FsZNUaFfGxfq2P+Z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\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8/Z0NvtP5nT+5yFqW853RcqKioti6dSsLFy7E09MTDw8Pjh8/TkpKCpaWltSoUYMxY8bw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22eb8384358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(embeddings, labels):\n",
    "  \n",
    "  n_clusters = 20 # number of clusters\n",
    "  # automatically build a discrete set of colors, each for cluster\n",
    "  label_colors = [pylab.cm.spectral(float(i) /n_clusters) for i in range(n_clusters)]\n",
    "  \n",
    "  assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  \n",
    "  # Define K-Means\n",
    "  kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=0).fit(embeddings)\n",
    "  kmeans_labels = kmeans.labels_\n",
    "  \n",
    "  pylab.figure(figsize=(15,15))  # in inches\n",
    "    \n",
    "  # plot all the embeddings and their corresponding words\n",
    "  for i, (label,klabel) in enumerate(zip(labels,kmeans_labels)):\n",
    "    x, y = embeddings[i,:]\n",
    "    pylab.scatter(x, y, c=label_colors[klabel])    \n",
    "        \n",
    "    pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
    "                   ha='right', va='bottom',fontsize=10)\n",
    "\n",
    "  # use for saving the figure if needed\n",
    "  #pylab.savefig('word_embeddings.png')\n",
    "  pylab.show()\n",
    "\n",
    "words = [reverse_dictionary[i] for i in selected_ids]\n",
    "plot(two_d_embeddings, words)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBOW Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the data generation process\n",
    "We need to define a new data generator for CBOW. Shape of the new input array is (batch_size, context_window*2). That is, a batch in CBOW captures all the words in the context of a given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "with window_size = 1:\n",
      "    batch: [['propaganda', 'a'], ['is', 'concerted'], ['a', 'set'], ['concerted', 'of'], ['set', 'messages'], ['of', 'aimed'], ['messages', 'at'], ['aimed', 'influencing']]\n",
      "    labels: ['is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at']\n",
      "\n",
      "with window_size = 2:\n",
      "    batch: [['propaganda', 'is', 'concerted', 'set'], ['is', 'a', 'set', 'of'], ['a', 'concerted', 'of', 'messages'], ['concerted', 'set', 'messages', 'aimed'], ['set', 'of', 'aimed', 'at'], ['of', 'messages', 'at', 'influencing'], ['messages', 'aimed', 'influencing', 'the'], ['aimed', 'at', 'the', 'opinions']]\n",
      "    labels: ['a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch_cbow(batch_size, window_size):\n",
    "    # window_size is the amount of words we're looking at from each side of a given word\n",
    "    # creates a single batch\n",
    "    \n",
    "    # data_index is updated by 1 everytime we read a set of data point\n",
    "    global data_index\n",
    "\n",
    "    # span defines the total window size, where\n",
    "    # data we consider at an instance looks as follows. \n",
    "    # [ skip_window target skip_window ]\n",
    "    # e.g if skip_window = 2 then span = 5\n",
    "    span = 2 * window_size + 1 # [ skip_window target skip_window ]\n",
    "\n",
    "    # two numpy arras to hold target words (batch)\n",
    "    # and context words (labels)\n",
    "    # Note that batch has span-1=2*window_size columns\n",
    "    batch = np.ndarray(shape=(batch_size,span-1), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    \n",
    "    # The buffer holds the data contained within the span\n",
    "    buffer = collections.deque(maxlen=span)\n",
    "\n",
    "    # Fill the buffer and update the data_index\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "\n",
    "    # Here we do the batch reading\n",
    "    # We iterate through each batch index\n",
    "    # For each batch index, we iterate through span elements\n",
    "    # to fill in the columns of batch array\n",
    "    for i in range(batch_size):\n",
    "        target = window_size  # target label at the center of the buffer\n",
    "        target_to_avoid = [ window_size ] # we only need to know the words around a given word, not the word itself\n",
    "\n",
    "        # add selected target to avoid_list for next time\n",
    "        col_idx = 0\n",
    "        for j in range(span):\n",
    "            # ignore the target word when creating the batch\n",
    "            if j==span//2:\n",
    "                continue\n",
    "            batch[i,col_idx] = buffer[j] \n",
    "            col_idx += 1\n",
    "        labels[i, 0] = buffer[target]\n",
    "\n",
    "        # Everytime we read a data point,\n",
    "        # we need to move the span by 1\n",
    "        # to create a fresh new span\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "\n",
    "    return batch, labels\n",
    "\n",
    "for window_size in [1,2]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch_cbow(batch_size=8, window_size=window_size)\n",
    "    print('\\nwith window_size = %d:' % (window_size))\n",
    "    print('    batch:', [[reverse_dictionary[bii] for bii in bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Hyperparameters\n",
    "\n",
    "Here we define several hyperparameters including `batch_size` (amount of samples in a single batch) `embedding_size` (size of embedding vectors) `window_size` (context window size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128 # Data points in a single batch\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "# How many words to consider left and right.\n",
    "# Skip gram by design does not require to have all the context words in a given step\n",
    "# However, for CBOW that's a requirement, so we limit the window size\n",
    "window_size = 2 \n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors\n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "# We sample valid datapoints randomly from a large window without always being deterministic\n",
    "valid_window = 50\n",
    "\n",
    "# When selecting valid examples, we select some of the most frequent words as well as\n",
    "# some moderately rare words as well\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "valid_examples = np.append(valid_examples,random.sample(range(1000, 1000+valid_window), valid_size),axis=0)\n",
    "\n",
    "num_sampled = 32 # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Inputs and Outputs\n",
    "\n",
    "Here we define placeholders for feeding in training inputs and outputs (each of size `batch_size`) and a constant tensor to contain validation examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Training input data (target word IDs). Note that it has 2*window_size columns\n",
    "train_dataset = tf.placeholder(tf.int32, shape=[batch_size,2*window_size])\n",
    "# Training input label data (context word IDs)\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "# Validation input data, we don't need a placeholder\n",
    "# as we have already defined the IDs of the words selected\n",
    "# as validation data\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Model Parameters and Other Variables\n",
    "We now define several TensorFlow variables such as an embedding layer (`embeddings`) and neural network parameters (`softmax_weights` and `softmax_biases`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables.\n",
    "\n",
    "# Embedding layer, contains the word embeddings\n",
    "embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0,dtype=tf.float32))\n",
    "\n",
    "# Softmax Weights and Biases\n",
    "softmax_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                 stddev=0.5 / math.sqrt(embedding_size),dtype=tf.float32))\n",
    "softmax_biases = tf.Variable(tf.random_uniform([vocabulary_size],0.0,0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Model Computations\n",
    "\n",
    "We first defing a lookup function to fetch the corresponding embedding vectors for a set of given inputs. Concretely, we define 2$\\times$`window_size` embedding lookups. We then concatenate all these looked up embedding vectors to form a matrix of size `[batch_size, embedding_size, 2*window_size]`. Thereafter, we average these embedding lookups to produce an average embeddings of size `[batch_size, embedding_size]`. With that, we define negative sampling loss function `tf.nn.sampled_softmax_loss` which takes in the embedding vectors and previously defined neural network parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defining 4 embedding lookups representing each word in the context\n",
      "Stacked embedding size: [128, 128, 4]\n",
      "Reduced mean embedding size: [128, 128]\n"
     ]
    }
   ],
   "source": [
    "# Model.\n",
    "# Look up embeddings for a batch of inputs.\n",
    "# Here we do embedding lookups for each column in the input placeholder\n",
    "# and then average them to produce an embedding_size word vector\n",
    "stacked_embedings = None\n",
    "print('Defining %d embedding lookups representing each word in the context'%(2*window_size))\n",
    "for i in range(2*window_size):\n",
    "    embedding_i = tf.nn.embedding_lookup(embeddings, train_dataset[:,i])        \n",
    "    x_size,y_size = embedding_i.get_shape().as_list()\n",
    "    if stacked_embedings is None:\n",
    "        stacked_embedings = tf.reshape(embedding_i,[x_size,y_size,1])\n",
    "    else:\n",
    "        stacked_embedings = tf.concat(axis=2,values=[stacked_embedings,tf.reshape(embedding_i,[x_size,y_size,1])])\n",
    "\n",
    "assert stacked_embedings.get_shape().as_list()[2]==2*window_size\n",
    "print(\"Stacked embedding size: %s\"%stacked_embedings.get_shape().as_list())\n",
    "mean_embeddings =  tf.reduce_mean(stacked_embedings,2,keepdims=False)\n",
    "print(\"Reduced mean embedding size: %s\"%mean_embeddings.get_shape().as_list())\n",
    "\n",
    "# Compute the softmax loss, using a sample of the negative labels each time.\n",
    "# inputs are embeddings of the train words\n",
    "# with this loss we optimize weights, biases, embeddings\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=mean_embeddings,\n",
    "                           labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameter Optimizer\n",
    "\n",
    "We then define a learning rate as a constant and an optimizer which uses the Adagrad method. Feel free to experiment with other optimizers listed [here](https://www.tensorflow.org/api_guides/python/train)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Optimizer.\n",
    "optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Word Similarities \n",
    "We calculate the similarity between two given words in terms of the cosine distance. To do this efficiently we use matrix operations to do so, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute the similarity between minibatch examples and all embeddings.\n",
    "# We use the cosine distance:\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the CBOW Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2000: 3.571350\n",
      "Average loss at step 4000: 3.077189\n",
      "Average loss at step 6000: 2.951976\n",
      "Average loss at step 8000: 2.868055\n",
      "Average loss at step 10000: 2.796585\n",
      "Nearest to with: nederlandse, abbr, canaan, ars, brochure, ester, comte, rubberized,\n",
      "Nearest to other: nuclear, 1419, restore, some, teammates, unrepresented, mothe, both,\n",
      "Nearest to '': hungarorum, paws, }, updrafts, hatta, dementing, ca, titled,\n",
      "Nearest to to: schmitz, norwood, would, bleak, 1855., arsonists, inferring, athletics,\n",
      "Nearest to on: ascendancy, dynamo, mcqueen, guyana, csc, on-die, tennessee, a.f.c.,\n",
      "Nearest to ): hitler, dart, gilma, jamboree, amp, funnier, 2,341, 35.5,\n",
      "Nearest to his: their, its, her, the, 's, sir, intestines, exaltation,\n",
      "Nearest to this: it, the, each, budgetary, predate, ingest, a, adequate,\n",
      "Nearest to one: p-38js, chekhov, rms, enforcing, degrees, assembled, daily, bytown,\n",
      "Nearest to which: but, stolen, unremarkable, who, −50, now, piero, where,\n",
      "Nearest to not: vegetative, also, sardis, hovered, canvas, opt, inoffensive, makarios,\n",
      "Nearest to but: however, which, extraordinary, multidimensional, footprints, though, who, transferred,\n",
      "Nearest to were: are, lech, was, molecular, defines, sintering, 900, fidel,\n",
      "Nearest to also: billionaires, not, hutchins, safe, ismail, highly, endorheic, qu,\n",
      "Nearest to had: has, have, checker, stacked, nutrient, fagatogo, defendants, was,\n",
      "Nearest to ;: ., ,, furry, seyyid, electromagnet, edible, between, f,\n",
      "Average loss at step 12000: 2.771984\n",
      "Average loss at step 14000: 2.716518\n",
      "Average loss at step 16000: 2.696995\n",
      "Average loss at step 18000: 2.683554\n",
      "Average loss at step 20000: 2.664762\n",
      "Nearest to with: ester, abbr, mid-to-late, plumes, toxteth, ars, checker, nederlandse,\n",
      "Nearest to other: some, nuclear, 1419, 600, occupying, spina, sandemanian, dye,\n",
      "Nearest to '': paws, updrafts, titled, ca, dementing, divining, asaph, firearm,\n",
      "Nearest to to: would, will, schmitz, norwood, could, bleak, seacoast, 535,\n",
      "Nearest to on: ascendancy, csc, upon, dynamo, mcqueen, guyana, invaded, on-die,\n",
      "Nearest to ): e.g, hitler, metres, jamboree, driving, unheard-of, 687., laterite,\n",
      "Nearest to his: her, their, its, 's, sir, intestines, exaltation, xor,\n",
      "Nearest to this: it, budgetary, each, predate, adequate, some, rapport, ingest,\n",
      "Nearest to one: enforcing, assembled, daily, extensive, smiling, immobile, 400-series, because,\n",
      "Nearest to which: but, where, who, that, expressions, unremarkable, polarization, −50,\n",
      "Nearest to not: n't, vegetative, sardis, opt, inoffensive, penthouse, spouses, erratically,\n",
      "Nearest to but: which, however, though, including, extraordinary, footprints, who, although,\n",
      "Nearest to were: are, was, lech, fidel, had, re-evaluation, have, reasoning,\n",
      "Nearest to also: billionaires, hutchins, ismail, highly, non-members, team, safe, alegre,\n",
      "Nearest to had: has, have, checker, was, stacked, were, fagatogo, received,\n",
      "Nearest to ;: ., ,, furry, :, electromagnet, arrange, jakub, kraje,\n",
      "Average loss at step 22000: 2.624746\n",
      "Average loss at step 24000: 2.615392\n",
      "Average loss at step 26000: 2.590478\n",
      "Average loss at step 28000: 2.499715\n",
      "Average loss at step 30000: 2.449202\n",
      "Nearest to with: abbr, toxteth, comte, ester, nederlandse, dubuque, raped, \\,\n",
      "Nearest to other: some, nuclear, 1419, teammates, pia, top-loading, these, dye,\n",
      "Nearest to '': titled, asaph, paws, updrafts, meshuggah, journeyed, huiñaimarca, [,\n",
      "Nearest to to: would, will, norwood, schmitz, could, seacoast, bleak, must,\n",
      "Nearest to on: upon, ascendancy, csc, guyana, dynamo, invaded, emil, muskogee,\n",
      "Nearest to ): e.g, hitler, amp, 687., orléans, unheard-of, metres, jamboree,\n",
      "Nearest to his: her, their, its, 's, kondo, intestines, sir, exaltation,\n",
      "Nearest to this: it, budgetary, each, predate, what, recklessness, some, ingest,\n",
      "Nearest to one: enforcing, assembled, smiling, 400-series, immobile, daily, because, batch,\n",
      "Nearest to which: but, where, who, −50, expressions, stolen, delineated, that,\n",
      "Nearest to not: n't, vegetative, sardis, still, penthouse, inoffensive, opt, erratically,\n",
      "Nearest to but: however, which, though, including, extraordinary, although, multidimensional, 0.006,\n",
      "Nearest to were: are, was, lech, fidel, been, had, reasoning, have,\n",
      "Nearest to also: billionaires, now, hutchins, alegre, non-members, ductile, ismail, cleveland,\n",
      "Nearest to had: has, have, checker, remained, having, carboxylic, received, were,\n",
      "Nearest to ;: ., ,, furry, :, electromagnet, -, arrange, —,\n",
      "Average loss at step 32000: 2.443845\n",
      "Average loss at step 34000: 2.432435\n",
      "Average loss at step 36000: 2.415099\n",
      "Average loss at step 38000: 2.385209\n",
      "Average loss at step 40000: 2.407546\n",
      "Nearest to with: comte, abbr, dubuque, mid-to-late, ester, between, ptolemaic, raped,\n",
      "Nearest to other: nuclear, teammates, these, dye, 1419, some, spina, pia,\n",
      "Nearest to '': paws, dementing, meshuggah, titled, [, huiñaimarca, subfreezing, divining,\n",
      "Nearest to to: would, norwood, will, schmitz, bleak, could, hypothermia, must,\n",
      "Nearest to on: upon, ascendancy, csc, mcqueen, a.f.c., nmsii, muskogee, transcripts,\n",
      "Nearest to ): e.g, hitler, 687., jamboree, amp, 2,341, driving, ciprofloxacin,\n",
      "Nearest to his: her, their, its, xor, kondo, intestines, sir, exaltation,\n",
      "Nearest to this: it, budgetary, predate, what, each, marge, some, similar,\n",
      "Nearest to one: enforcing, 400-series, assembled, smiling, daily, batch, multiculturalism, formally,\n",
      "Nearest to which: where, but, who, that, stolen, 1440, −50, expressions,\n",
      "Nearest to not: n't, vegetative, sardis, still, opt, penthouse, erratically, spouses,\n",
      "Nearest to but: however, though, which, including, especially, although, particularly, extraordinary,\n",
      "Nearest to were: are, was, lech, fidel, being, been, re-evaluation, sintering,\n",
      "Nearest to also: billionaires, now, alegre, cleveland, non-members, still, resting, hutchins,\n",
      "Nearest to had: has, have, having, carboxylic, remained, was, checker, dunkirk,\n",
      "Nearest to ;: ., ,, furry, :, venation, expressionistic, electromagnet, seyyid,\n",
      "Average loss at step 42000: 2.369765\n",
      "Average loss at step 44000: 2.388248\n",
      "Average loss at step 46000: 2.374272\n",
      "Average loss at step 48000: 2.355751\n",
      "Average loss at step 50000: 2.389159\n",
      "Nearest to with: abbr, toxteth, mid-to-late, ester, 102,800, vernal, raped, connelly,\n",
      "Nearest to other: these, various, top-loading, dye, some, teammates, pia, modern,\n",
      "Nearest to '': divining, titled, dementing, paws, mcalmon, subfreezing, [, neubrandenburg,\n",
      "Nearest to to: would, will, must, schmitz, could, bleak, towards, norwood,\n",
      "Nearest to on: upon, ascendancy, csc, muskogee, dynamo, badenweiler, transcripts, mcqueen,\n",
      "Nearest to ): e.g, metres, driving, orléans, ciprofloxacin, unheard-of, 687., 81.3,\n",
      "Nearest to his: her, their, its, xor, my, 's, intestines, exaltation,\n",
      "Nearest to this: it, budgetary, each, 98.6, what, predate, recklessness, tyresö,\n",
      "Nearest to one: enforcing, smiling, 400-series, batch, claudius, assembled, recovered, daily,\n",
      "Nearest to which: where, but, −50, that, polarization, what, who, stolen,\n",
      "Nearest to not: n't, vegetative, still, sardis, rarely, penthouse, opt, erratically,\n",
      "Nearest to but: though, however, which, although, especially, and, including, footprints,\n",
      "Nearest to were: are, was, lech, being, re-evaluation, been, fidel, unclassified,\n",
      "Nearest to also: now, billionaires, cleveland, still, ductile, alegre, highly, arrays,\n",
      "Nearest to had: has, have, having, remained, checker, carboxylic, downplayed, dunkirk,\n",
      "Nearest to ;: ., ,, —, -, furry, :, expressionistic, arrange,\n",
      "Average loss at step 52000: 2.324526\n",
      "Average loss at step 54000: 2.288432\n",
      "Average loss at step 56000: 2.252855\n",
      "Average loss at step 58000: 2.250210\n",
      "Average loss at step 60000: 2.234358\n",
      "Nearest to with: abbr, nederlandse, dubuque, vernal, validate, ptolemaic, irreparable, comte,\n",
      "Nearest to other: dye, various, teammates, top-loading, pia, tributaries, these, woolshed,\n",
      "Nearest to '': huiñaimarca, titled, divining, paws, [, meshuggah, subfreezing, война,\n",
      "Nearest to to: would, will, schmitz, could, bleak, must, norwood, towards,\n",
      "Nearest to on: upon, ascendancy, csc, feces, muskogee, mcqueen, transcripts, badenweiler,\n",
      "Nearest to ): e.g, driving, orléans, unheard-of, 2,341, amp, 687., mazzola,\n",
      "Nearest to his: her, their, its, my, xor, intestines, 's, kondo,\n",
      "Nearest to this: it, budgetary, tyresö, 98.6, predate, similar, garcia, marge,\n",
      "Nearest to one: enforcing, 400-series, assembled, claudius, smiling, multiculturalism, recovered, flourish,\n",
      "Nearest to which: where, but, whose, what, −50, stolen, who, whom,\n",
      "Nearest to not: n't, vegetative, still, sardis, rarely, opt, erratically, penthouse,\n",
      "Nearest to but: however, though, which, especially, although, including, particularly, phosgene,\n",
      "Nearest to were: are, lech, was, re-evaluation, being, been, unclassified, fidel,\n",
      "Nearest to also: now, billionaires, still, cleveland, alegre, resting, counter-terrorism, exterminate,\n",
      "Nearest to had: has, have, having, remained, carboxylic, was, dunkirk, checker,\n",
      "Nearest to ;: ., ,, —, commutes, :, venation, expressionistic, electromagnet,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 62000: 2.253141\n",
      "Average loss at step 64000: 2.204824\n",
      "Average loss at step 66000: 2.238768\n",
      "Average loss at step 68000: 2.210738\n",
      "Average loss at step 70000: 2.214753\n",
      "Nearest to with: 21., abbr, irreparable, dubuque, mid-to-late, toxteth, ptolemaic, ester,\n",
      "Nearest to other: various, dye, pia, woolshed, top-loading, zwan, teammates, stoic,\n",
      "Nearest to '': divining, paws, subfreezing, titled, война, dementing, meshuggah, [,\n",
      "Nearest to to: will, schmitz, bleak, norwood, contradict, must, would, might,\n",
      "Nearest to on: upon, ascendancy, csc, metropolises, mcqueen, transcripts, cheer, emil,\n",
      "Nearest to ): e.g, orléans, metres, mazzola, driving, 687., 2,341, jamboree,\n",
      "Nearest to his: her, their, its, my, xor, osiris, intestines, exaltation,\n",
      "Nearest to this: it, similar, 98.6, budgetary, tyresö, predate, these, citation,\n",
      "Nearest to one: enforcing, 400-series, rejoin, batch, smiling, recovered, assembled, claudius,\n",
      "Nearest to which: where, but, whose, whom, polarization, −50, stolen, what,\n",
      "Nearest to not: n't, vegetative, still, rarely, opt, sardis, penthouse, never,\n",
      "Nearest to but: however, though, which, especially, although, including, footprints, particularly,\n",
      "Nearest to were: are, was, lech, been, re-evaluation, sintering, being, fidel,\n",
      "Nearest to also: now, still, billionaires, cleveland, mimesis, ductile, exterminate, resting,\n",
      "Nearest to had: have, has, having, remained, dunkirk, carboxylic, downplayed, were,\n",
      "Nearest to ;: ., ,, —, expressionistic, -, :, conch, terveet,\n",
      "Average loss at step 72000: 2.221417\n",
      "Average loss at step 74000: 2.200340\n",
      "Average loss at step 76000: 2.236453\n",
      "Average loss at step 78000: 2.179140\n",
      "Average loss at step 80000: 2.130204\n",
      "Nearest to with: between, abbr, dubuque, toxteth, 102,800, mid-to-late, 21., raped,\n",
      "Nearest to other: pia, zwan, top-loading, various, woolshed, teammates, tributaries, dye,\n",
      "Nearest to '': titled, subfreezing, huiñaimarca, divining, paws, война, [, hiphop,\n",
      "Nearest to to: schmitz, will, towards, must, would, bleak, toward, norwood,\n",
      "Nearest to on: upon, ascendancy, csc, feces, 27th, muskogee, emil, mcqueen,\n",
      "Nearest to ): e.g, orléans, metres, driving, 687., mazzola, amp, 2,341,\n",
      "Nearest to his: her, their, its, my, osiris, xor, 's, /tr,\n",
      "Nearest to this: it, 98.6, tyresö, budgetary, iguanodon, citation, similar, predate,\n",
      "Nearest to one: enforcing, recovered, glens, flourish, rejoin, blackbody, 400-series, claudius,\n",
      "Nearest to which: where, but, whom, whose, stolen, −50, 1440, who,\n",
      "Nearest to not: n't, vegetative, rarely, still, never, sardis, opt, erratically,\n",
      "Nearest to but: however, though, which, especially, although, including, particularly, al-arab,\n",
      "Nearest to were: are, was, lech, sintering, fidel, been, unclassified, re-evaluation,\n",
      "Nearest to also: now, still, billionaires, cleveland, neo-classicism, mimesis, ductile, counter-terrorism,\n",
      "Nearest to had: have, having, has, carboxylic, dunkirk, remained, checker, downplayed,\n",
      "Nearest to ;: ., ,, —, expressionistic, furry, :, -, conch,\n",
      "Average loss at step 82000: 2.135085\n",
      "Average loss at step 84000: 2.124107\n",
      "Average loss at step 86000: 2.126267\n",
      "Average loss at step 88000: 2.101563\n",
      "Average loss at step 90000: 2.100460\n",
      "Nearest to with: dubuque, comte, abbr, toxteth, nederlandse, validate, 21., ptolemaic,\n",
      "Nearest to other: various, woolshed, dye, pia, teammates, zwan, individual, top-loading,\n",
      "Nearest to '': война, paws, subfreezing, huiñaimarca, titled, dementing, divining, classrooms,\n",
      "Nearest to to: norwood, schmitz, helped, towards, bleak, must, seacoast, hypothermia,\n",
      "Nearest to on: upon, ascendancy, csc, muskogee, 27th, procedure, mcqueen, nmsii,\n",
      "Nearest to ): e.g, mazzola, orléans, 687., nightfall, 2,341, ciprofloxacin, driving,\n",
      "Nearest to his: her, their, my, its, osiris, xor, our, converging,\n",
      "Nearest to this: it, similar, 98.6, tyresö, these, marge, citation, utilitarianism,\n",
      "Nearest to one: enforcing, recovered, glens, rejoin, 400-series, assembled, batch, multiculturalism,\n",
      "Nearest to which: where, but, whom, whose, stolen, −50, 1440, moneda,\n",
      "Nearest to not: n't, rarely, never, still, vegetative, sardis, opt, erratically,\n",
      "Nearest to but: though, however, which, although, especially, including, particularly, footprints,\n",
      "Nearest to were: are, was, lech, sintering, re-evaluation, been, fidel, unclassified,\n",
      "Nearest to also: now, still, billionaires, cleveland, neo-classicism, mimesis, resting, actually,\n",
      "Nearest to had: have, has, having, carboxylic, remained, dunkirk, leikin, checker,\n",
      "Nearest to ;: ., ,, —, expressionistic, conch, commutes, furry, venation,\n",
      "Average loss at step 92000: 2.102314\n",
      "Average loss at step 94000: 2.097561\n",
      "Average loss at step 96000: 2.110340\n",
      "Average loss at step 98000: 2.095927\n",
      "Average loss at step 100000: 2.108117\n",
      "Nearest to with: toxteth, 21., 102,800, dubuque, mid-to-late, vernal, validate, abbr,\n",
      "Nearest to other: woolshed, various, top-loading, pia, dye, these, zwan, teammates,\n",
      "Nearest to '': divining, titled, subfreezing, dementing, война, word, neubrandenburg, hiphop,\n",
      "Nearest to to: bleak, might, schmitz, helped, towards, norwood, will, must,\n",
      "Nearest to on: upon, ascendancy, 27th, csc, rotherhithe, feces, cheer, muskogee,\n",
      "Nearest to ): e.g, orléans, mazzola, driving, metres, nightfall, 687., 2,341,\n",
      "Nearest to his: her, their, my, its, osiris, xor, intestines, digitally,\n",
      "Nearest to this: it, 98.6, similar, citation, tyresö, budgetary, marge, these,\n",
      "Nearest to one: glens, enforcing, recovered, 400-series, claudius, rejoin, surviving, smiling,\n",
      "Nearest to which: where, but, whom, whose, −50, stolen, moneda, polarization,\n",
      "Nearest to not: n't, rarely, never, vegetative, still, opt, sardis, spouses,\n",
      "Nearest to but: however, though, which, although, especially, whereas, including, footprints,\n",
      "Nearest to were: are, lech, was, sintering, been, re-evaluation, unclassified, symbolizing,\n",
      "Nearest to also: now, still, billionaires, cleveland, neo-classicism, resting, ductile, zemin,\n",
      "Nearest to had: having, has, have, dunkirk, downplayed, carboxylic, hancock, remained,\n",
      "Nearest to ;: ., ,, —, conch, expressionistic, :, commutes, storing,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "cbow_losses = []\n",
    "\n",
    "# ConfigProto is a way of providing various configuration settings \n",
    "# required to execute the graph\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as session:\n",
    "    \n",
    "    # Initialize the variables in the graph\n",
    "    tf.global_variables_initializer().run()\n",
    "    print('Initialized')\n",
    "    \n",
    "    average_loss = 0\n",
    "    \n",
    "    # Train the Word2vec model for num_step iterations\n",
    "    for step in range(num_steps):\n",
    "        \n",
    "        # Generate a single batch of data\n",
    "        batch_data, batch_labels = generate_batch_cbow(batch_size, window_size)\n",
    "        \n",
    "        # Populate the feed_dict and run the optimizer (minimize loss)\n",
    "        # and compute the loss\n",
    "        feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "        _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "        \n",
    "        # Update the average loss variable\n",
    "        average_loss += l\n",
    "        \n",
    "        if (step+1) % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss = average_loss / 2000\n",
    "                # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            cbow_losses.append(average_loss)\n",
    "            print('Average loss at step %d: %f' % (step+1, average_loss))\n",
    "            average_loss = 0\n",
    "            \n",
    "        # Evaluating validation set word similarities\n",
    "        if (step+1) % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            # Here we compute the top_k closest words for a given validation word\n",
    "            # in terms of the cosine distance\n",
    "            # We do this for all the words in the validation set\n",
    "            # Note: This is an expensive step\n",
    "            for i in range(valid_size):\n",
    "                valid_word = reverse_dictionary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log = 'Nearest to %s:' % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = reverse_dictionary[nearest[k]]\n",
    "                    log = '%s %s,' % (log, close_word)\n",
    "                print(log)\n",
    "    cbow_final_embeddings = normalized_embeddings.eval()\n",
    "    \n",
    "\n",
    "np.save('cbow_embeddings',cbow_final_embeddings)\n",
    "\n",
    "with open('cbow_losses.csv', 'wt') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    writer.writerow(cbow_losses)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
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   "source": [
    "## Final Remarks\n",
    "I'm grateful to the Google's free deep learning course at Udacity, as some of the helper functions were adopted from code files provided in their [Github repo](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/udacity/5_word2vec.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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